| An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis Z Peng, N Kessissoglou Wear 255 (7-12), 1221-1232, 2003 | 240 | 2003 |
| A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques Z Peng, NJ Kessissoglou, M Cox Wear 258 (11-12), 1651-1662, 2005 | 184 | 2005 |
| Expert system development for vibration analysis in machine condition monitoring S Ebersbach, Z Peng Expert systems with applications 34 (1), 291-299, 2008 | 178 | 2008 |
| Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis Z Li, X Yan, Z Tian, C Yuan, Z Peng, L Li Measurement 46 (1), 259-271, 2013 | 173 | 2013 |
| The investigation of the condition and faults of a spur gearbox using vibration and wear debris analysis techniques S Ebersbach, Z Peng, NJ Kessissoglou Wear 260 (1-2), 16-24, 2006 | 173 | 2006 |
| Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method Z Li, X Yan, C Yuan, Z Peng, L Li Mechanical Systems and Signal Processing 25 (7), 2589-2607, 2011 | 171 | 2011 |
| The use of the fractal description to characterize engineering surfaces and wear particles CQ Yuan, J Li, XP Yan, Z Peng Wear 255 (1-6), 315-326, 2003 | 169 | 2003 |
| Wear performance of UHMWPE and reinforced UHMWPE composites in arthroplasty applications: a review JC Baena, J Wu, Z Peng Lubricants 3 (2), 413-436, 2015 | 158 | 2015 |
| Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations Z Li, Y Jiang, Q Guo, C Hu, Z Peng Renewable Energy 116, 55-73, 2018 | 157 | 2018 |
| Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms Q Liu, H Wu, MJ Paul, P He, Z Peng, B Gludovatz, JJ Kruzic, CH Wang, ... Acta Materialia 201, 316-328, 2020 | 151 | 2020 |
| Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review Z Li, Y Jiang, C Hu, Z Peng Measurement 90, 4-19, 2016 | 143 | 2016 |
| An RFID-based remote monitoring system for enterprise internal production management S Zhou, W Ling, Z Peng The International Journal of Advanced Manufacturing Technology 33, 837-844, 2007 | 138 | 2007 |
| Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks J Shi, D Peng, Z Peng, Z Zhang, K Goebel, D Wu Mechanical Systems and Signal Processing 162, 107996, 2022 | 135 | 2022 |
| Development of a gear vibration indicator and its application in gear wear monitoring C Hu, WA Smith, RB Randall, Z Peng Mechanical Systems and Signal Processing 76, 319-336, 2016 | 135 | 2016 |
| Vibration-based updating of wear prediction for spur gears K Feng, P Borghesani, WA Smith, RB Randall, ZY Chin, J Ren, Z Peng Wear 426, 1410-1415, 2019 | 125 | 2019 |
| Wear particle classification in a fuzzy grey system Z Peng, TB Kirk Wear 225, 1238-1247, 1999 | 121 | 1999 |
| Progress and trend of sensor technology for on-line oil monitoring TH Wu, HK Wu, Y Du, ZX Peng Science China Technological Sciences 56, 2914-2926, 2013 | 117 | 2013 |
| Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools WA Smith, P Borghesani, Q Ni, K Wang, Z Peng Mechanical Systems and Signal Processing 134, 106303, 2019 | 116 | 2019 |
| Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference WA Smith, Z Fan, Z Peng, H Li, RB Randall Mechanical Systems and Signal Processing 75, 371-394, 2016 | 108 | 2016 |
| Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features T Wu, H Wu, Y Du, N Kwok, Z Peng Wear 316 (1-2), 19-29, 2014 | 108 | 2014 |
Wade SmithSenior Lecturer, University of New South WalesVerified email at unsw.edu.au
